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Anticipated synchronization in human EEG data: Unidirectional causality with negative phase lag.

Francisco-Leandro P Carlos1, Maciel-Monteiro Ubirakitan2,3, Marcelo Cairrão Araújo Rodrigues2

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Summary

Scientists discovered anticipated synchronization (AS) in human brain signals using electroencephalogram (EEG) during a GO/NO-GO task. This finding challenges traditional methods of inferring brain information flow direction.

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Area of Science:

  • Neuroscience
  • Brain-computer interfaces
  • Signal processing

Background:

  • Functional brain connectivity is crucial for understanding neural processes.
  • Relative phase differences and coherence patterns are commonly used to infer information flow direction.
  • A synchronized regime, anticipated synchronization (AS), has been observed where phase differences do not reflect causality.

Purpose of the Study:

  • Investigate coherence and causality in human electroencephalogram (EEG) signals during a GO/NO-GO task.
  • Verify the existence of anticipated synchronization (AS) in human EEG data.
  • Analyze phase relations in EEG signals to understand synchronization patterns.

Main Methods:

  • Analysis of electroencephalogram (EEG) signals during a GO/NO-GO task.
  • Investigation of coherence and causality at the alpha frequency band (∼10 Hz).
  • Examination of phase differences and synchronization patterns between electrode pairs.

Main Results:

  • Human EEG signals demonstrate anticipated synchronization (AS), where a later signal influences an earlier one.
  • The study confirms AS in EEG for the first time in the human brain.
  • Both AS and delayed synchronization (DS) were observed, with diverse phase relations including in-phase, antiphase, and out-of-phase synchronization.

Conclusions:

  • Anticipated synchronization (AS) is a valid phenomenon in human EEG, challenging conventional causality inference.
  • The findings necessitate a re-evaluation of how information flow is determined from brain signals.
  • EEG analysis reveals complex synchronization dynamics, including both leading and lagging relationships between brain regions.